Readme rewrite (#12615)

Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

templates/sql-ollama/README.md

@@ -1,18 +1,78 @@
-# SQL with LLaMA2 using llama.cpp
+# sql-ollama
 
-This template allows you to chat with a SQL database in natural language in private, using an open source LLM.
+This template enables a user to interact with a SQL database using natural language. 
 
-## Set up Ollama
+It uses [Zephyr-7b](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) via [Ollama](https://ollama.ai/library/zephyr) to run inference locally on a Mac laptop.
 
-Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
+## Environment Setup
 
-Also follow instructions to download your LLM of interest:
+Before using this template, you need to set up Ollama and SQL database.
 
-* This template uses `llama2:13b-chat`
-* But you can pick from many LLMs [here](https://ollama.ai/library)
+1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
 
-## Set up SQL DB
+2. Download your LLM of interest:
 
-This template includes an example DB of 2023 NBA rosters.
+    * This package uses `zephyr`: `ollama pull zephyr`
+    * You can choose from many LLMs [here](https://ollama.ai/library)
 
-You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).
+3. This package includes an example DB of 2023 NBA rosters. You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb).
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U "langchain-cli[serve]"
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package sql-ollama
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add sql-ollama
+```
+
+And add the following code to your `server.py` file:
+
+```python
+from sql_ollama import chain as sql_ollama_chain
+
+add_routes(app, sql_ollama_chain, path="/sql-ollama")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/sql-ollama/playground](http://127.0.0.1:8000/sql-ollama/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/sql-ollama")
+```